compute.sigma.star: Computes intermediate (tetrachoric) correlation matrix

In BinNor: Simultaneous Generation of Multivariate Binary and Normal Variates

Description

This function computes the intermediate correlation matrix by combining tetrachoric correlation for binary-binary combinations, biserial correlations for binary-normal combinations and Pearson correlation for normal-normal combinations. If the resulting correlation matrix is not positive definite, a nearest positive matrix will be used.

Usage

compute.sigma.star(no.bin, no.nor, prop.vec.bin = NULL,
		 corr.vec = NULL, corr.mat = NULL)

Arguments

no.bin	Number of binary variables
no.nor	Number of normal variables
prop.vec.bin	Probability vector for binary variables
corr.vec	Vector of elements below the diagonal of correlation matrix ordered columnwise
corr.mat	Specified correlation matrix

Value

sigma_star	A resulting intermediate correlation matrix Σ^*
nonPD	If a resulting intermediate correlation matrix is non-positive definite, it is stored in this value. Otherwise it is NULL.
PD	TRUE if Σ^* is positive definite, FALSE otherwise. A FALSE indicates that the nearest positive definite matrix is returned.
eigenv	Eigenvalues of the Σ^* before the conversion

See Also

validation.corr, nearPD, phi2poly, is.positive.definite,
jointly.generate.binary.normal, simulation

Examples

cmat = lower.tri.to.corr.mat(corr.vec= c(0.16, 0.04, 0.38, 0.14, 0.47, 0.68),4)
compute.sigma.star(no.bin=2, no.nor=2, prop.vec.bin=c(0.4,0.7), 
corr.vec=NULL,corr.mat=cmat)

BinNor documentation built on May 29, 2017, 3:52 p.m.